Long-term cost-utility analysis of family therapy vs. treatment as usual for young people seen after self-harm.

BACKGROUND: The joint evidence of the cost and the effectiveness of family-based therapies is modest. OBJECTIVE: To study the cost-effectiveness of family therapy (FT) versus treatment-as-usual (TAU) for young people seen after self-harm combining data from an 18-month trial and hospital records up to 60-month from randomisation. METHODS: We estimate the cost-effectiveness of FT compared to TAU over 5 years using a quasi-Markov state model based on self-harm hospitalisations where probabilities of belonging in a state are directly estimated from hospital data. The primary outcome is quality-adjusted life years (QALY). Cost perspective is NHS and PSS and includes treatment costs, health care use, and hospital attendances whether it is for self-harm or not. Incremental cost-effectiveness ratios are calculated and deterministic and probabilistic sensitivity analyses are conducted. RESULTS: Both trial arms show a significant decrease in hospitalisations over the 60-month follow-up. In the base case scenario, FT participants incur higher costs (mean +£1,693) and negative incremental QALYs (-0.01) than TAU patients. The associated ICER at 5 years is dominated and the incremental health benefit at the £30,000 per QALY threshold is -0.067. Probabilistic Sensitivity Analysis finds the probability that FT is cost-effective is around 3 - 2% up to a maximum willingness to pay of £50,000 per QALY. This suggest that the extension of the data to 60 months show no difference in effectiveness between treatments. CONCLUSION: Whilst extended trial follow-up from routinely collected statistics is useful to improve the modelling of longer-term cost-effectiveness, FT is not cost-effective relative to TAU and dominated in a cost-utility analysis.

Harmonising electronic health records for reproducible research: challenges, solutions and recommendations from a UK-wide COVID-19 research collaboration.

BACKGROUND: The CVD-COVID-UK consortium was formed to understand the relationship between COVID-19 and cardiovascular diseases through analyses of harmonised electronic health records (EHRs) across the four UK nations. Beyond COVID-19, data harmonisation and common approaches enable analysis within and across independent Trusted Research Environments. Here we describe the reproducible harmonisation method developed using large-scale EHRs in Wales to accommodate the fast and efficient implementation of cross-nation analysis in England and Wales as part of the CVD-COVID-UK programme. We characterise current challenges and share lessons learnt. METHODS: Serving the scope and scalability of multiple study protocols, we used linked, anonymised individual-level EHR, demographic and administrative data held within the SAIL Databank for the population of Wales. The harmonisation method was implemented as a four-layer reproducible process, starting from raw data in the first layer. Then each of the layers two to four is framed by, but not limited to, the characterised challenges and lessons learnt. We achieved curated data as part of our second layer, followed by extracting phenotyped data in the third layer. We captured any project-specific requirements in the fourth layer. RESULTS: Using the implemented four-layer harmonisation method, we retrieved approximately 100 health-related variables for the 3.2 million individuals in Wales, which are harmonised with corresponding variables for > 56 million individuals in England. We processed 13 data sources into the first layer of our harmonisation method: five of these are updated daily or weekly, and the rest at various frequencies providing sufficient data flow updates for frequent capturing of up-to-date demographic, administrative and clinical information. CONCLUSIONS: We implemented an efficient, transparent, scalable, and reproducible harmonisation method that enables multi-nation collaborative research. With a current focus on COVID-19 and its relationship with cardiovascular outcomes, the harmonised data has supported a wide range of research activities across the UK.

Routine hospital data - is it good enough for trials? An example using England's Hospital Episode Statistics in the SHIFT trial of Family Therapy vs. Treatment as Usual in adolescents following self-harm.

BACKGROUND: Use of routine data sources within clinical research is increasing and is endorsed by the National Institute for Health Research to increase trial efficiencies; however there is limited evidence for its use in clinical trials, especially in relation to self-harm. One source of routine data, Hospital Episode Statistics, is collated and distributed by NHS Digital and contains details of admissions, outpatient, and Accident and Emergency attendances provided periodically by English National Health Service hospitals. We explored the reliability and accuracy of Hospital Episode Statistics, compared to data collected directly from hospital records, to assess whether it would provide complete, accurate, and reliable means of acquiring hospital attendances for self-harm - the primary outcome for the SHIFT (Self-Harm Intervention: Family Therapy) trial evaluating Family Therapy for adolescents following self-harm. METHODS: Participant identifiers were linked to Hospital Episode Statistics Accident and Emergency, and Admissions data, and episodes combined to describe participants' complete hospital attendance. Attendance data were initially compared to data previously gathered by trial researchers from pre-identified hospitals. Final comparison was conducted of subsequent attendances collected through Hospital Episode Statistics and researcher follow-up. Consideration was given to linkage rates; number and proportion of attendances retrieved; reliability of Accident and Emergency, and Admissions data; percentage of self-harm episodes recorded and coded appropriately; and percentage of required data items retrieved. RESULTS: Participants were first linked to Hospital Episode Statistics with an acceptable match rate of 95%, identifying a total of 341 complete hospital attendances, compared to 139 reported by the researchers at the time. More than double the proportion of Hospital Episode Statistics Accident and Emergency episodes could not be classified in relation to self-harm (75%) compared to 34.9% of admitted episodes, and of overall attendances, 18% were classified as self-harm related and 20% not related, while ambiguity or insufficient information meant 62% were unclassified. Of 39 self-harm-related attendances reported by the researchers, Hospital Episode Statistics identified 24 (62%) as self-harm related while 15 (38%) were unclassified. Based on final data received, 1490 complete hospital attendances were identified and comparison to researcher follow-up found Hospital Episode Statistics underestimated the number of self-harm attendances by 37.2% (95% confidence interval 32.6%-41.9%). CONCLUSION: Advantages of routine data collection via NHS Digital included the acquisition of more comprehensive and timely trial outcome data, identifying more than double the number of hospital attendances than researchers. Disadvantages included ambiguity in the classification of self-harm relatedness. Our resulting primary outcome data collection strategy used routine data to identify hospital attendances supplemented by targeted researcher data collection for attendances requiring further self-harm classification.

Identification of heart failure hospitalization from NHS Digital data: comparison with expert adjudication.

AIMS: Population-wide, person-level, linked electronic health record data are increasingly used to estimate epidemiology, guide resource allocation, and identify events in clinical trials. The accuracy of data from NHS Digital (now part of NHS England) for identifying hospitalization for heart failure (HHF), a key HF standard, is not clear. This study aimed to evaluate the accuracy of NHS Digital data for identifying HHF. METHODS AND RESULTS: Patients experiencing at least one HHF, as determined by NHS Digital data, and age- and sex-matched patients not experiencing HHF, were identified from a prospective cohort study and underwent expert adjudication. Three code sets commonly used to identify HHF were applied to the data and compared with expert adjudication (I50: International Classification of Diseases-10 codes beginning I50; OIS: Clinical Commissioning Groups Outcomes Indicator Set; and NICOR: National Institute for Cardiovascular Outcomes Research, used as the basis for the National Heart Failure Audit in England and Wales). Five hundred four patients underwent expert adjudication, of which 10 (2%) were adjudicated to have experienced HHF. Specificity was high across all three code sets in the first diagnosis position {I50: 96.2% [95% confidence interval (CI) 94.1-97.7%]; NICOR: 93.3% [CI 90.8-95.4%]; OIS: 95.6% [CI 93.3-97.2%]} but decreased substantially as the number of diagnosis positions expanded. Sensitivity [40.0% (CI 12.2-73.8%)] and positive predictive value (PPV) [highest with I50: 17.4% (CI 8.1-33.6%)] were low in the first diagnosis position for all coding sets. PPV was higher for the National Heart Failure Audit criteria, albeit modestly [36.4% (CI 16.6-62.2%)]. CONCLUSIONS: NHS Digital data were not able to accurately identify HHF and should not be used in isolation for this purpose.

Use of routinely collected health data in randomised clinical trials: comparison of trial-specific death data in the BOSS trial with NHS Digital data.

BACKGROUND: A promising approach to reduce the increasing costs of clinical trials is the use of routinely collected health data as participant data. However, the quality of this data could limit its usability as trial participant data. METHODS: The BOSS trial is a randomised controlled trial comparing regular endoscopies versus endoscopies at need in patients with Barrett's oesophagus with primary endpoint death. Data on death and cancer collected every 2 years after randomisation (trial-specific data) were compared to data received annually (all patients on one date) from the routinely collected health data source National Health Service (NHS) Digital. We investigated completeness, agreement and timeliness and looked at the implications for the primary trial outcome. Completeness and agreement were assessed by evaluating the number of reported and missing cases and any disparities between reported dates. Timeliness was considered by graphing the year a death was first reported in the trial-specific data against that for NHS Digital data. Implications on the primary trial outcome, overall survival, of using one of the data sources alone were investigated using Kaplan-Meier graphs. To assess the utility of cause of death and cancer diagnoses, oesophageal cancer cases were compared. RESULTS: NHS Digital datasets included more deaths and often reported them sooner than the trial-specific data. The number reported as being from oesophageal cancer was similar in both datasets. Due to time lag in reporting and missing cases, the event rate appeared higher using the NHS Digital data. CONCLUSION: NHS Digital death data is useful for calculating overall survival where trial-specific follow-up is only every 2 years from randomisation and the follow-up requires patient response. The cancer data was not a large enough sample to assess usability. We suggest that this assessment of registry data is done for more phase III RCTs and for more registry data to get a more complete picture of when RCHD would be useful in phase III RCT. TRIAL REGISTRATION: ISRCTN54190466 (BOSS) 1 Oct 2009.